Meta’s Muse Strategy Extends From AI App to Wearables
Meta puts Muse at the centre of a consumer AI push
Meta’s personal AI agent, Muse, is reportedly exceeding ChatGPT’s early mobile-launch figures, while the company prepares to bring the product to smart glasses and a small Tamagotchi-style device. The development was a central subject of TechCrunch’s Equity podcast, where Kirsten Korosec, Anthony Ha and Sean O’Kane examined Meta’s consumer AI strategy and its implications for startups.
Muse is positioned as more than a standalone AI app. Meta is directing its consumer reach toward an agent that could appear across different devices, including smart glasses and the compact hardware concept described by TechCrunch as Tamagotchi-like. That approach places the agent in a broader product environment rather than limiting it to a single mobile interface.
A crowded week for frontier models
The discussion unfolded during a concentrated period of model releases. Anthropic rolled out Opus 5.5, and OpenAI announced GPT-6 model updates 90 minutes later. The sequence followed public debate among AI leaders at OpenAI and Anthropic about “pacing the frontier,” raising questions about what that pace looks like in a market where major releases can arrive close together.
Against that backdrop, Meta drew attention not only for model development but for consumer distribution. TechCrunch framed Muse’s reported early growth as a notable contrast with the model announcements from OpenAI and Anthropic, while also highlighting Meta’s plans to connect the agent with new forms of hardware.
What the strategy means for AI companies
The Equity panel considered what Meta’s approach could mean for startups building on top of AI technology. Its discussion ranged from where investment is flowing to the AI products that might become part of everyday life. It also addressed a16z’s new school for aspiring founders and two separate AI and hardware deals.
For businesses evaluating AI, Muse illustrates the importance of looking beyond a launch headline. The relevant question is whether an agent can be made useful through the devices and product contexts people already use. Teams should therefore assess AI plans in terms of daily workflow fit, distribution and the practical role hardware may play alongside the underlying model.